Skip to main content
SpringerLink
Log in

Autonomous clustering using rough set theory

  • Published:
International Journal of Automation and Computing Aims and scope Submit manuscript

Abstract

This paper proposes a clustering technique that minimizes the need for subjective human intervention and is based on elements of rough set theory (RST). The proposed algorithm is unified in its approach to clustering and makes use of both local and global data properties to obtain clustering solutions. It handles single-type and mixed attribute data sets with ease. The results from three data sets of single and mixed attribute types are used to illustrate the technique and establish its efficiency.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. T. Sorensen. A Method of Establishing Groups of Equal Amplitude in Plant Sociology Based on Similarity of Species Content and Its Application to the Analyses of the Vegetation on Danish Commons. Biologiske Skrifter, vol. 5, no. 4, pp. 1–34, 1948.

    Google Scholar 

  2. P. Sneath. The Application of Computers to Taxonomy. Journal of General Microbiology, vol. 17, no. 1, pp. 201–226, 1957.

    Google Scholar 

  3. R. R. Sokal, P. H. A. Sneath. Principles of Numerical Taxonomy, W. H. Freeman, San Francisco, USA, 1963.

    Google Scholar 

  4. J. H. Ward. Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, vol. 58, no. 301, pp. 236–244, 1963.

    Article  MathSciNet  Google Scholar 

  5. M. R. Anderberg. Cluster Analysis for Applications, Academic Press, New York, USA, 1973.

    MATH  Google Scholar 

  6. M. S. Aldenderfer, R. K. Blashfield. Cluster Analysis, Sage University Paper, Newbury Park, USA, 1984.

    Google Scholar 

  7. B. S. Everitt. Cluster Analysis, Edward Arnold, Cambridge, UK, 1993.

    Google Scholar 

  8. S. Sharma. Applied Multivariate Techniques, John Wiley & Sons, New York, USA, 1996.

    Google Scholar 

  9. A. K. Jain, M. N. Murty, P. J. Flynn. Data Clustering: A review. ACM Computing Surveys, vol. 31, no.3, pp. 264–323, 1999.

    Article  Google Scholar 

  10. R. R. Yegar. Intelligent Control of the Hierarchical Clustering Process. IEEE Transactions on Systems, Man, and Cybenetics-Part B, vol. 30, no. 6, pp. 835–845, 2000.

    Article  Google Scholar 

  11. E. W. Forgey. Cluster Analysis of Multivariate Data: Efficiency Versus Interpretability of Classifications. Biometrics, vol. 21, no. 3, pp. 768–769, 1965.

    Google Scholar 

  12. R. C. Jancey. Multidimensional Group Analysis. Australian Journal of Botany, vol. 14, no. 1, pp. 127–130, 1966.

    Article  Google Scholar 

  13. J. B. MacQueen. Some Methods of Classification and Analysis of Multivariate Observations. In Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, University of California Press, Berkeley, USA, vol. 1, pp. 281–297, 1967.

    Google Scholar 

  14. G. H. Ball, D. J. Hall. ISODATA, a Novel Method of Data Analysis and Pattern Classification, Technical Report AD 699616, Stanford Research Institute, Menlo Park, USA, 1965.

    Google Scholar 

  15. F. H. C. Marriott. Optimization Methods of Cluster Analysis. Biometrika, vol. 69, no. 2, pp. 417–421, 1982.

    Article  MathSciNet  Google Scholar 

  16. S. Z. Selim, M. A. Ismail. K-means Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality. IEEE Transactions Pattern Analysis and Machine Intelligence, vol. 6, no. 1, pp. 81–87, 1984.

    Article  MATH  Google Scholar 

  17. J. C. Dunn. A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact, Well Separated Clusters. Journal of Cybernetics, vol. 3, no. 3, pp. 32–57, 1973.

    MATH  MathSciNet  Google Scholar 

  18. J. C. Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithm, Plenum Press, New York, USA, 1981.

    Google Scholar 

  19. A. K. Jain, R. C. Dubes. Algorithms for Clustering Data, Prentice-Hall, USA, 1988.

    MATH  Google Scholar 

  20. M. S. Kamel, S. Z. Selim. New Algorithms for Solving the Fuzzy Clustering Problem. Pattern Recognition, vol. 27, no. 3, pp. 421–428, 1994.

    Article  Google Scholar 

  21. J. S. R. Jang, C. T. Sun, E. Mizutani. Neuro-fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice-Hall, USA, 1996.

    Google Scholar 

  22. F. Höppner, F. Klawonn, R. Kruse, T. Runkler. Fuzzy Cluster Analysis. Wiley & Sons, Chichester, England, 1999.

    MATH  Google Scholar 

  23. Z. Pawlak. Rough Sets. International Journal of Information and Computer Sciences, vol. 11, no. 5, pp. 341–356, 1982.

    Article  MathSciNet  MATH  Google Scholar 

  24. Z. Pawlak. Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic, Dordrecht, Holland, 1991.

    MATH  Google Scholar 

  25. A. Skowron, C. Rauszer. The Discernibility Matrices and Functions in Information Systems. Intelligent Decision Support, Handbook of Applications and Advances of the Rough Sets Theory, R. Slowinski (ed.), Kluwer Academic, Dordrecht, Holland, pp. 331–362, 1992.

    Google Scholar 

  26. J. Komorowski, Z. Pawlak, L. Polkowski, A. Skowron. Rough Sets: A Tutorial. Rough Fuzzy Hybridization: A New Method for Decision Making, S. Pal, A. Skowron (eds.), Springer, Berlin, Germany, 1998.

    Google Scholar 

  27. D. Dubois, H. Prade. Rough Fuzzy Sets and Fuzzy Rough Sets. International Jounal of General Systems, vol. 17, no. 2, pp. 191–209, 1989.

    Article  Google Scholar 

  28. C. L. Bean, C. Kambhampati. Knowledge-oriented Clustering for Decision Support. In Proceedings of IEEE International Joint Conference on Neural Networks, Portland, Oregon, USA, vol. 4, pp. 3244–3249, 2003.

    Google Scholar 

  29. T. Okuzaki, S. Hirano, S. Kobashi, Y. Hata, Y. Takahashi. A Rough Set Based Clustering Method by Knowledge Combination. IEICE Transactions on Information and Systems, vol. 85, no. 12, pp. 1898–1908, 2002.

    Google Scholar 

  30. C. L. Bean, C. Kambhampati, S. Rajasekharan. A Rough Set Solution to a Fuzzy Set Problem. In Proceedings of IEEE International Conference on Fuzzy Systems, World Congress in Computational Intelligence, Honolulu, Hawaii, vol. 1, pp. 18–23, 2002.

    Article  Google Scholar 

  31. S. Hirano, S. Tsumoto. A Knowledge-oriented Clustering Technique Based on Rough Sets. In Proceedings of 25th IEEE International Conference on Computer and Software Applications, Chicago, USA, pp. 632–637, 2001.

  32. B. J. F. Manly. Multivariate Statistical Methods, A Primer, Chapman & Hall, New York, USA, 2000.

    Google Scholar 

  33. J. A. Hartigan. Clustering Algorithms, John Wiley & Sons, New York, USA, 1975.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Warwick Medical School Gibbet Hill Campus, University of Warwick, Coventry, CV4 7AL, UK

    Charlotte Bean

  2. Department of Computer Science, University of Hull, Hull, HU6 7RX, UK

    Chandra Kambhampati

Authors
  1. Charlotte Bean
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chandra Kambhampati
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chandra Kambhampati.

Additional information

Charlotte Bean received her Master of mathematics (MMath) degree from the University of Hull, UK, in 1999 and the Ph.D. degree in computer science from the same university in 2004. She is currently a research fellow in the Medical School at the University of Warwick, UK.

Her research interests include data analysis, the theoretical development of techniques and algorithms used in statistical modeling and data mining, especially using tools such as rough set theory, FST, and artificial neural networks.

Chandra Kambhampati received his Ph.D. degree from City University, London, for his dissertation on algorithms for optimizing control in 1988. He hold positions in the Department of Cybernetics, at the University of Reading, and the Department of Chemical and Process Engineering, at the University of Newcastle Upon Tyne. He is currently a reader in the Department of Computer Science at the University of Hull. He also heads the Neural, Emergent and Agent Technologies (NEAT) Group, and has a number of doctoral and graduate students, and research associates. He has authored and co-authored over 100 papers on optimization, adaptive optimization, optimal control, neural networks, and fuzzy logic for control and robotics. He is a member of IEEE and IEE.

His research interests include fault tolerant control, networked control systems, signal processing, neural networks, and multiagent systems.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bean, C., Kambhampati, C. Autonomous clustering using rough set theory. Int. J. Autom. Comput. 5, 90–102 (2008). https://doi.org/10.1007/s11633-008-0090-3

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11633-008-0090-3

Keywords

Search

Navigation